實驗報告材料 聚類分析報告

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1、word實驗報告 聚類分析實驗原理：K均值聚類、中心點聚類、系統(tǒng)聚類和EM算法聚類分析技術(shù)。實驗題目：用鳶尾花的數(shù)據(jù)集，進(jìn)行聚類挖掘分析。實驗要求：探索鳶尾花數(shù)據(jù)的基本特征，利用不同的聚類挖掘方法，獲得基本結(jié)論并簡明解釋。實驗題目-分析報告：data(iris) rm(list=ls() gc() used (Mb) gc trigger (Mb) max used (Mb)Ncells 431730 23.1 929718 49.7 607591 32.5Vcells 787605 6.1 8388608 64.0 1592403 12.2 data(iris) data head(data

2、) Sepal.Length Sepal.Width Petal.Length Petal.Width Species1 5.1 3.5 1.4 0.2 setosa2 4.9 3.0 1.4 0.2 setosa3 4.7 3.2 1.3 0.2 setosa4 4.6 3.1 1.5 0.2 setosa5 5.0 3.6 1.4 0.2 setosa6 5.4 3.9 1.7 0.4 setosa#Kmean聚類分析 newiris newiris$Species (kc table(iris$Species, kc$cluster) 1 2 3 setosa 0 50 0 versic

3、olor 48 0 2 virginica 14 0 36 plot(newirisc(Sepal.Length, Sepal.Width), col = kc$cluster) points(kc$centers,c(Sepal.Length, Sepal.Width), col = 1:3, pch = 8, cex=2)#K-Mediods 進(jìn)行聚類分析 install.packages(cluster) library(cluster) iris.pam table(iris$Species,iris.pam$clustering) 1 2 3 setosa 50 0 0 versic

4、olor 0 3 47 virginica 0 49 1 layout(matrix(c(1,2),1,2) plot(iris.pam) layout(matrix(1)#hc iris.hc plot( iris.hc, hang = -1) plclust( iris.hc, labels = FALSE, hang = -1) re iris.id sapply(unique(iris.id),+ function(g)iris$Speciesiris.id=g)1 1 setosa setosa setosa setosa setosa setosa setosa setosa se

5、tosa setosa setosa12 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa23 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa34 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa45 setosa setosa setosa setosa setosa setosaL

6、evels: setosa versicolor virginica2 1 versicolor versicolor versicolor versicolor versicolor versicolor versicolor 8 versicolor versicolor versicolor versicolor versicolor versicolor versicolor15 versicolor versicolor versicolor versicolor versicolor versicolor versicolor22 versicolor versicolor vir

7、ginica virginica virginica virginica virginica 29 virginica virginica virginica virginica virginica virginica virginica 36 virginica virginica virginica virginica virginica virginica virginica 43 virginica virginica virginica virginica virginica virginica virginica 50 virginica virginica virginica v

8、irginica virginica virginica virginica 57 virginica virginica virginica virginica virginica virginica virginica 64 virginica virginica virginica virginica virginica virginica virginica 71 virginica virginica Levels: setosa versicolor virginica3 1 versicolor versicolor versicolor versicolor versicolo

9、r versicolor versicolor 8 versicolor versicolor versicolor versicolor versicolor versicolor versicolor15 versicolor versicolor versicolor versicolor versicolor versicolor versicolor22 versicolor versicolor versicolor versicolor versicolor versicolor virginica Levels: setosa versicolor virginica plot

10、(iris.hc) rect.hclust(iris.hc,k=4,border=light grey)#用淺灰色矩形框出4分類聚類結(jié)果 rect.hclust(iris.hc,k=3,border=dark grey)#用淺灰色矩形框出3分類聚類結(jié)果 rect.hclust(iris.hc,k=7,which=c(2,6),border=dark grey)# DBSCAN #基于密度的聚類 install.packages(fpc) library(fpc) ds1=dbscan(iris,1:4,eps=1,MinPts=5)#半徑參數(shù)為1，密度閾值為5 ds1dbscan Pts=15

11、0 MinPts=5 eps=1 1 2border 0 1seed 50 99total 50 100 ds2=dbscan(iris,1:4,eps=4,MinPts=5) ds3=dbscan(iris,1:4,eps=4,MinPts=2) ds4=dbscan(iris,1:4,eps=8,MinPts=2) par(mfcol=c(2,2) plot(ds1,iris,1:4,main=1: MinPts=5 eps=1) plot(ds3,iris,1:4,main=3: MinPts=2 eps=4) plot(ds2,iris,1:4,main=2: MinPts=5 eps

12、=4) plot(ds4,iris,1:4,main=4: MinPts=2 eps=8) d=dist(iris,1:4)#計算數(shù)據(jù)集的距離矩陣d max(d);min(d)#計算數(shù)據(jù)集樣本的距離的最值1 7.0851961 0 install.packages(ggplot2) library(ggplot2) interval=cut_interval(d,30) table(interval)interval 0,0.236 (0.236,0.472 (0.472,0.709 (0.709,0.945 (0.945,1.18 (1.18,1.42 88 585 876 891 831

13、688 (1.42,1.65 (1.65,1.89 (1.89,2.13 (2.13,2.36 (2.36,2.6 (2.6,2.83 543 369 379 339 335 406 (2.83,3.07 (3.07,3.31 (3.31,3.54 (3.54,3.78 (3.78,4.01 (4.01,4.25 458 459 465 480 468 505 (4.25,4.49 (4.49,4.72 (4.72,4.96 (4.96,5.2 (5.2,5.43 (5.43,5.67 349 385 321 291 187 (5.67,5.9 (5.9,6.14 (6.14,6.38 (6.

14、38,6.61 (6.61,6.85 (6.85,7.09 97 92 78 50 18 4 which.max(table(interval)(0.709,0.945 4 for(i in 3:5)+ for(j in 1:10)+ ds=dbscan(iris,1:4,eps=i,MinPts=j)+ print(ds)+ + dbscan Pts=150 MinPts=1 eps=3 1seed 150total 150dbscan Pts=150 MinPts=2 eps=3 1seed 150total 150dbscan Pts=150 MinPts=3 eps=3 1seed 1

15、50total 150dbscan Pts=150 MinPts=4 eps=3 1seed 150total 150dbscan Pts=150 MinPts=5 eps=3 1seed 150total 150dbscan Pts=150 MinPts=6 eps=3 1seed 150total 150dbscan Pts=150 MinPts=7 eps=3 1seed 150total 150dbscan Pts=150 MinPts=8 eps=3 1seed 150total 150dbscan Pts=150 MinPts=9 eps=3 1seed 150total 150d

16、bscan Pts=150 MinPts=10 eps=3 1seed 150total 150dbscan Pts=150 MinPts=1 eps=4 1seed 150total 150dbscan Pts=150 MinPts=2 eps=4 1seed 150total 150dbscan Pts=150 MinPts=3 eps=4 1seed 150total 150dbscan Pts=150 MinPts=4 eps=4 1seed 150total 150dbscan Pts=150 MinPts=5 eps=4 1seed 150total 150dbscan Pts=1

17、50 MinPts=6 eps=4 1seed 150total 150dbscan Pts=150 MinPts=7 eps=4 1seed 150total 150dbscan Pts=150 MinPts=8 eps=4 1seed 150total 150dbscan Pts=150 MinPts=9 eps=4 1seed 150total 150dbscan Pts=150 MinPts=10 eps=4 1seed 150total 150dbscan Pts=150 MinPts=1 eps=5 1seed 150total 150dbscan Pts=150 MinPts=2

18、 eps=5 1seed 150total 150dbscan Pts=150 MinPts=3 eps=5 1seed 150total 150dbscan Pts=150 MinPts=4 eps=5 1seed 150total 150dbscan Pts=150 MinPts=5 eps=5 1seed 150total 150dbscan Pts=150 MinPts=6 eps=5 1seed 150total 150dbscan Pts=150 MinPts=7 eps=5 1seed 150total 150dbscan Pts=150 MinPts=8 eps=5 1seed

19、 150total 150dbscan Pts=150 MinPts=9 eps=5 1seed 150total 150dbscan Pts=150 MinPts=10 eps=5 1seed 150total 150#30次dbscan的聚類結(jié)果 ds5=dbscan(iris,1:4,eps=3,MinPts=2) ds6=dbscan(iris,1:4,eps=4,MinPts=5) ds7=dbscan(iris,1:4,eps=5,MinPts=9) par(mfcol=c(1,3) plot(ds5,iris,1:4,main=1: MinPts=2 eps=3) plot(ds

20、6,iris,1:4,main=3: MinPts=5 eps=4) plot(ds7,iris,1:4,main=2: MinPts=9 eps=5)# EM 期望最大化聚類 install.packages(mclust) library(mclust) fit_EM=Mclust(iris,1:4)fitting . |=| 100% summary(fit_EM)- Gaussian finite mixture model fitted by EM algorithm - Mclust VEV (ellipsoidal, equal shape) model with 2 ponen

21、ts: log.likelihood n df BIC ICL -215.726 150 26 -561.7285 -561.7289Clustering table: 1 2 50 100 summary(fit_EM,parameters=TRUE)- Gaussian finite mixture model fitted by EM algorithm - Mclust VEV (ellipsoidal, equal shape) model with 2 ponents: log.likelihood n df BIC ICL -215.726 150 26 -561.7285 -5

22、61.7289Clustering table: 1 2 50 100 Mixing probabilities: 1 2 0.3333319 0.6666681 Means: ,1 ,2Sepal.Length 5.0060022 6.261996Sepal.Width 3.4280049 2.871999Petal.Length 1.4620007 4.905992Petal.Width 0.2459998 1.675997Variances:,1 Sepal.Length Sepal.Width Petal.Length Petal.WidthSepal.Length 0.1506511

23、4 0.13080115 0.02084463 0.01309107Sepal.Width 0.13080115 0.17604529 0.01603245 0.01221458Petal.Length 0.02084463 0.01603245 0.02808260 0.00601568Petal.Width 0.01309107 0.01221458 0.00601568 0.01042365,2 Sepal.Length Sepal.Width Petal.Length Petal.WidthSepal.Length 0.4000438 0.10865444 0.3994018 0.14

24、368256Sepal.Width 0.1086544 0.10928077 0.1238904 0.07284384Petal.Length 0.3994018 0.12389040 0.6109024 0.25738990Petal.Width 0.1436826 0.07284384 0.2573899 0.16808182 plot(fit_EM)#對EM聚類結(jié)果作圖Model-based clustering plots: 1: BIC2: classification3: uncertainty4: densitySelection:（下面顯示選項）#選1#選2#選3#選4Sele

25、ction: 0 iris_BIC=mclustBIC(iris,1:4)fitting . |=| 100% iris_BICsum=summary(iris_BIC,data=iris,1:4) iris_BICsum #獲取數(shù)1據(jù)集iris在各模型和類別數(shù)下的BIC值Best BIC values: VEV,2 VEV,3 VVV,2BIC -561.7285 -562.5522369 -574.01783BIC diff 0.0000 -0.8237748 -12.28937Classification table for model (VEV,2): 1 2 50 100 iris_

26、BICBayesian Information Criterion (BIC): EII VII EEI VEI EVI VVI EEE1 -1804.0854 -1804.0854 -1522.1202 -1522.1202 -1522.1202 -1522.1202 -829.97822 -1123.4117 -1012.2352 -1042.9679 -956.2823 -1007.3082 -857.5515 -688.09723 -878.7650 -853.8144 -813.0504 -779.1566 -797.8342 -744.6382 -632.96474 -893.61

27、40 -812.6048 -827.4036 -748.4529 -837.5452 -751.0198 -646.02585 -782.6441 -742.6083 -741.9185 -688.3463 -766.8158 -711.4502 -604.81316 -715.7136 -705.7811 -693.7908 -676.1697 -774.0673 -707.2901 -609.85437 -731.8821 -698.5413 -713.1823 -680.7377 -813.5220 -766.6500 -632.49478 -725.0805 -701.4806 -69

28、1.4133 -679.4640 -740.4068 -764.1969 -639.26409 -694.5205 -700.0276 -696.2607 -702.0143 -767.8044 -755.8290 -653.0878 EVE VEE VVE EEV VEV EVV VVV1 -829.9782 -829.9782 -829.9782 -829.9782 -829.9782 -829.9782 -829.97822 -657.2263 -656.3270 -605.1841 -644.5997 -561.7285 -658.3306 -574.01783 -666.5491 -

29、605.3982 -636.4259 -644.7810 -562.5522 -656.0359 -580.83964 -705.5435 -604.8371 -639.7078 -699.8684 -602.0104 -725.2925 -630.60005 -723.7199 NA -632.2056 -652.2959 -634.2890 NA -676.60616 -661.9497 -609.5584 -664.8224 -664.4537 -679.5116 NA -754.79387 -699.5102 NA -690.6108 -709.9530 -704.7699 -809.

30、8276 -806.92778 -700.4277 -654.8237 -709.9392 -735.4463 -712.8788 -831.7520 -830.63739 -729.6651 NA -734.2997 -758.9348 -748.8237 -882.4391 -883.6931Top 3 models based on the BIC criterion: VEV,2 VEV,3 VVV,2 -561.7285 -562.5522 -574.0178 par(mfcol=c(1,1) plot(iris_BIC,G=1:7,col=yellow) mclust2Dplot(

31、iris,1:2, + classification=iris_BICsum$classification,+ parameters=iris_BICsum$parameters,col=yellow) iris_Dens=densityMclust(iris,1:2)# 對每一個樣本進(jìn)行密度估計fitting . |=| 100% iris_DensdensityMclust model object: (VEV,2) Available ponents: 1 call data modelName n 5 d G BIC bic 9 loglik df hypvol parameters

32、13 z classification uncertainty density plot(iris_Dens,iris,1:2,col=yellow,nlevels=55) #輸入1或2Model-based density estimation plots: 1: BIC2: densitySelection:（下面顯示選項）#選1#選2Selection: 0 plot(iris_Dens,type = persp,col = grey(0.8)Model-based density estimation plots: 1: BIC2: densitySelection:（下面顯示選項）#選1#選2Selection: 028 / 28